halcon识别验证码，先训练后识别-编程知识

识别验证码图片，使用halcon 21.05
下面代码识别准确率100%

训练，图片打标签

* 
* Training of the OCR
* The font is used in "bottle.hdev"
* 
* 
* Step 0: Preparations* 
* Step 1: Segmentation
FontName:='yan_zheng_ma119'* 训练文件保存
TrainingFileName:='E:/Work/'+FontName+'.trf'* 分类器保存
FontFileOMC:='E:/Work/'+FontName+'.omc'dev_update_window ('off')
*训练
* 1  ,18   
* 30
*  已经训练 
* 已经训练了39，40,52* 图29、54 重点识别
*   read_image (Image, 'E:/Work/验证码图片/9.jpg')   
read_image (Image, 'E:/Work/验证码图片1/744.jpg')*获取图片尺寸
get_image_size (Image, Width, Height)
dev_close_window ()
dev_open_window (0, 0, 4 * Width, 7 * Height, 'black', WindowID)
* 设置打印字符的大小
set_display_font (WindowID, 80, 'mono', 'true', 'false')*放大图片
zoom_image_size(Image,ImageZoom,4 * Width,4 * Height ,'constant')*创建指定区域
gen_rectangle1 (Rectangle, 36, 57, 189, 588)
* 获取图片指定区域的图片
reduce_domain(ImageZoom,Rectangle,imgMask)* 根据阈值选取图像，筛选体积大的，数字越大体积越大，红色为选中
*threshold (Image, RawSegmentation, 200, 300)
threshold (imgMask, RawSegmentation, 0.1, 160)*connection (circleRegion, ConnectedRegions)*内部充满
fill_up (RawSegmentation, RegionFillUp)*char_threshold (Image, Image, Characters, 3, 50, Threshold)*根据形状特征填充孔洞*fill_up_shape(RawSegmentation, fillRegin, 'area', 5, 5)*去掉小的模块
*opening_rectangle1 (RegionFillUp, Cut,2,2)*利用矩形结构元素执行开运算。矩形宽设为1，高为7，相当于低于7的连接被截断
*opening_rectangle1 (RegionFillUp, Cut,3, 3)*分割粘连字符  10，30，，60，80    不行：90，100；宽度79，74，84，71* 20 等号污染了问号；* per 55识别成功29、54*partition_dynamic(circleRegion,parRegion,70,55)* 成功拆分，图片9*partition_dynamic(circleRegion,parRegion,55,50)* 成功拆分，图片11* partition_dynamic(circleRegion,parRegion,55,55)* 成功拆分，图片29   *partition_dynamic(circleRegion,parRegion,55,70)*成功划分7.jpg  `*partition_dynamic(circleRegion,parRegion,60,70)*成功划分998.jpg    *  partition_dynamic(circleRegion,parRegion,60,50)*將單個字符暈染連成一塊，水平、垂直方向延伸粘连closing_rectangle1 (RegionFillUp, RegionClosing1,1, 25)* 开口可以消除小区域（比圆形结构元件小）并平滑区域的边界。
opening_circle (RegionClosing1, circleRegion,5)*着色connection (circleRegion, ConnPatterns)*分割 ，默认以 partition_dynamic分割，能正确分割大多数图片情况；
*partition_dynamic(RegionFillUp,parRegion,60,50)partition_rectangle (ConnPatterns, par2, 76, 150)
partition_dynamic(ConnPatterns,par1,60,80)*着色对象个数count_obj (par1, par1_count)count_obj (par2, par2_count)AgainCheck:=0* 检查两个对象分割的块个数是否一致if ( par1_count != par2_count )partition_rectangle (ConnPatterns, par1, 76, 150)AgainCheck:=1endif* 第二次检查，每个分割块的宽度，是否在允许范围？if (AgainCheck==0)count_obj (par1, par1_count)*select_obj调用之前，必须调用sort_regionsort_region (par1, SortedRegions2, 'first_point', 'true', 'column')
*                select_obj (SortedRegions2, CharRegion, 1)
*                select_obj (SortedRegions2, CharRegion, 2)
*                select_obj (SortedRegions2, CharRegion, 3)
*                select_obj (SortedRegions2, CharRegion, 4)
*                select_obj (SortedRegions2, CharRegion, 5)              for P := 1 to par1_count   by 1select_obj (SortedRegions2, CharRegion, P )* 获取region的宽度region_features (CharRegion, 'width', CharWidth)if (CharWidth < 46  or  CharWidth>107 )partition_rectangle (ConnPatterns, par1, 76, 150)breakendifendforendif*  与threshold后的图,求交集
intersection (par1, RawSegmentation, NumberCandidates)* 选择指定尺寸的块
select_shape (NumberCandidates, Numbers, ['width','height'], 'and',[40, 10],[100,150])*着色对象个数
* count_obj (Numbers, Check_count)
* 如果着色对象数不足5个，说明分割有问题
* if ( Check_count!=5)
*       partition_rectangle (ConnPatterns, par2, 76, 150) 
*       intersection (par2, RawSegmentation, NumberCandidates)
*       select_shape (NumberCandidates, Numbers, ['width','height'], 'and',[40, 10],[100,150])
* endifsort_region (Numbers, SortedRegions, 'first_point', 'true', 'column')
dev_display (imgMask)
dev_set_color ('green')
dev_set_line_width (2)
dev_set_shape ('rectangle1')
dev_set_draw ('margin')
dev_display (SortedRegions)
* 
stop()*----------------训练模型，开始-----------------------
TrainingNames := ['1','/','1','=' ,'?']
*TrainingFileName := FontName + '.trf'
shape_trans (SortedRegions, RegionTrans, 'rectangle1')
area_center (RegionTrans, Area, Row, Column)
MeanRow := mean(Row)
dev_set_check ('~give_error')
*delete_file (TrainingFileName)
dev_set_check ('give_error')*图片数字下标
K:=0
for I := 0 to |Column| - 1 by 1select_obj (SortedRegions, CharaterRegions, I+1)* 获取region的宽度*  region_features (CharaterRegions, 'width', CharWidth)gen_empty_region (EmptyObject)     if(CharaterRegions!=EmptyObject)          append_ocr_trainf (CharaterRegions, imgMask, TrainingNames[K], TrainingFileName)disp_message (WindowID, TrainingNames[K], 'window', MeanRow+180, Column[I]-26, 'magenta', 'false') K:=K+1endifendfor*----------------训练模型，结束-----------------------

使用代码创建分类器；

在所有的样本图片都打上标签后，就可以训练多层感知分类器了


TrainFile:='E:/Work/yan_zheng_ma119.trf'
FontFileOMC:='E:/Work/yan_zheng_ma119.omc'*读取训练文件的字符
read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount)*使用多层感知器创建OCR分类器。
create_ocr_class_mlp (8, 10, 'constant', 'default', CharacterNames, 20,'none', 81, 42, OCRHandle)*训练OCR分类器。
trainf_ocr_class_mlp (OCRHandle, TrainFile, 100, 0.01, 0.01, Error, ErrorLog)*写入分类器文件保存
write_ocr_class_mlp (OCRHandle, FontFileOMC)

识别验证码，检验识别效果

* 
* Step 1: Segmentation
FontName:='yan_zheng_ma119'* 分类器保存
FontFileOMC:='E:/Work/'+FontName+'.omc'dev_update_window ('off')*训练* 重点识别：图29、54、9，11，** 循环图片文件列表，一个个识别*读取目录里面的文件list_files ('E:/Work/验证码3', ['files' ], ImageFiles)for ImgIndex := 1 to |ImageFiles|  by 1*for ImgIndex := 1 to 1000  by 1 fileName:=ImageFiles[ImgIndex - 1]read_image (Image, fileName) 
*   read_image (Image, 'E:/Work/验证码图片/29.jpg')* 拆分字符串tuple_split(fileName,'\\',fileNameShort)*取最后一个字符串fileShortName:=fileNameShort[5]*文件数量数fileCount := |ImageFiles|* 待处理文件数fileCount_surplus  := |ImageFiles|-ImgIndexget_image_size (Image, Width, Height)
dev_close_window ()
dev_open_window (0, 0, 4 * Width, 7 * Height, 'black', WindowID)
* 设置显示字体大小
set_display_font (WindowID, 27, 'mono', 'true', 'false')zoom_image_size(Image,ImageZoom,4 * Width,4 * Height ,'constant')*显示图片序号
disp_message (WindowID, fileShortName+',剩余待处理数'+fileCount_surplus+'/'+fileCount, 'window', 0, 0, 'magenta', 'false') *regiongrowing (ImageInvert, Regions, 2, 2, 1, 255)
*gen_rectangle1 (Rectangle, 4.875, 24.125, 46.625, 141.875)
gen_rectangle1 (Rectangle, 36, 57, 189, 588)
reduce_domain(ImageZoom,Rectangle,imgMask)* 筛选体积大的，数字越大体积越大，红色为选中
threshold (imgMask, RawSegmentation, 0.1, 160)*内部充满
fill_up (RawSegmentation, RegionFillUp)*char_threshold (Image, Image, Characters, 3, 50, Threshold)*根据形状特征填充孔洞*fill_up_shape(RawSegmentation, fillRegin, 'area', 5, 5)*去掉小的模块
*opening_rectangle1 (RegionFillUp, Cut,2,2)*利用矩形结构元素执行开运算。矩形宽设为1，高为7，相当于低于7的连接被截断
*opening_rectangle1 (RegionFillUp, Cut,3, 3)*將單個字符暈染連成一塊，水平、垂直方向延伸粘连closing_rectangle1 (RegionFillUp, RegionClosing1,1, 25)* 开口可以消除小区域（比圆形结构元件小）并平滑区域的边界。
opening_circle (RegionClosing1, circleRegion,5)*着色connection (circleRegion, ConnPatterns)*分割粘连字符 ，默认以 partition_dynamic分割，能正确分割大多数图片情况；
*partition_dynamic(RegionFillUp,parRegion,60,50)partition_rectangle (ConnPatterns, par2, 76, 150)
partition_dynamic(ConnPatterns,par1,60,80)*着色对象个数count_obj (par1, par1_count)count_obj (par2, par2_count)AgainCheck:=0* 检查两个对象分割的块个数是否一致if ( par1_count != par2_count )partition_rectangle (ConnPatterns, par1, 76, 150)AgainCheck:=1endif* 第二次检查，每个分割块的宽度，是否在允许范围？if (AgainCheck==0)count_obj (par1, par1_count)*select_obj调用之前，必须调用sort_regionsort_region (par1, SortedRegions2, 'first_point', 'true', 'column')
*                select_obj (SortedRegions2, CharRegion, 1)
*                select_obj (SortedRegions2, CharRegion, 2)
*                select_obj (SortedRegions2, CharRegion, 3)
*                select_obj (SortedRegions2, CharRegion, 4)
*                select_obj (SortedRegions2, CharRegion, 5)              for P := 1 to par1_count   by 1select_obj (SortedRegions2, CharRegion, P )* 获取region的宽度region_features (CharRegion, 'width', CharWidth)if (CharWidth < 46  or  CharWidth>107 )partition_rectangle (ConnPatterns, par1, 76, 150)breakendifendforendif*  与threshold后的图,求交集
intersection (par1, RawSegmentation, NumberCandidates)* 选择指定尺寸的块
select_shape (NumberCandidates, Numbers, ['width','height'], 'and',[40, 10],[100,150])*着色对象个数
* count_obj (Numbers, Check_count)
* 如果着色对象数不足5个，说明分割有问题
* if ( Check_count!=5)
*       partition_rectangle (ConnPatterns, par2, 76, 150) 
*       intersection (par2, RawSegmentation, NumberCandidates)
*       select_shape (NumberCandidates, Numbers, ['width','height'], 'and',[40, 10],[100,150])
* endifsort_region (Numbers, SortedRegions, 'first_point', 'true', 'column')
dev_display (imgMask)
dev_set_color ('green')
dev_set_line_width (2)
dev_set_shape ('rectangle1')
dev_set_draw ('margin')
dev_display (SortedRegions)
* 
* stop()shape_trans (SortedRegions, RegionTrans, 'rectangle1')
area_center (RegionTrans, Area, Row, Column)
MeanRow := mean(Row)
dev_set_check ('~give_error')dev_set_check ('give_error')* 设置显示字体大小
set_display_font (WindowID, 80, 'mono', 'true', 'false')*----------------识别 ，开始 -----------------------
read_ocr_class_mlp (FontFileOMC, OCRHandle2)*图片数字下标
K:=0
for I := 0 to |Column| - 1 by 1select_obj (SortedRegions, CharaterRegions, I+1)gen_empty_region (EmptyObject)     if(CharaterRegions!=EmptyObject)          do_ocr_multi_class_mlp (CharaterRegions, imgMask, OCRHandle2, Class, Confidence)disp_message (WindowID, Class, 'window', MeanRow+180, Column[I]-26, 'magenta', 'false') K:=K+1endif* 不打印第二个数字右侧的 =，？if( I==2 )breakendifendfor* 等待0.5秒钟wait_seconds (0.5)* 循环图片结束endfor*----------------识别 ，结束-----------------------